Abstract
In this work, a priori information about the nominal geometry of a measured object and the operating principles of three optical form measurement techniques are used to improve system performance compared to using each technique individually. More specifically, we present a surface form measurement system which uses artificial intelligence and machine vision to enable the efficient combination of fringe projection, photogrammetry and deflectometry. The measurement system can identify the regions of an object that is optimally measured by each individual process and employs a measurement strategy in which the measurement systems are combined in concert to achieve a complete three-dimensional measurement of the object using a reference camera. The system has a target maximum permissible error of 50 μm and the prototype demonstrates the ability to measure complex geometries of additively manufactured objects, with a maximum size of (10 × 10 × 10) cm, with minimal user input.
This work is sponsored by EPSRC grants EP/M008983/1 and EP/L016567/1 and the EU Framework Programme for Research and Innovation – Horizon 2020 – Grant Agreement No 721383.
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Acknowledgment
We would like to thank Fanis Ampatzidis from the University of Nottingham for providing the carbon fibre sample and Alex Jackson-Crisp for helping with the machining of parts for this setup.
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Stavroulakis, P. et al. (2019). External Shape Measurement for Industrial Applications Using Artificial Intelligence and Optimised Data Fusion. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_96
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DOI: https://doi.org/10.1007/978-3-030-01057-7_96
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